Skip to content
Write a review
CourseraArtificial IntelligenceFree courseCertificate

Machine Learning Foundations: A Case Study Approach

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

4.8
978 reviews

Created by: Carlos Guestrin

icon
Quality Score

Content Quality
/
Video Quality
/
Qualified Instructor
/
Course Pace
/
Course Depth & Coverage
/

Overall Score : 96 / 100

icon
Course Description

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications.Learning Outcomes: By the end of this course, you will be able to:-Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering.-Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.-Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task.-Utilize a dataset to fit a model to analyze new data.-Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
Read More product length iconTime: 24h

icon
Instructor Details

placeholder

Carlos Guestrin is the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington. He is also a co-founder and CEO of Dato, Inc., focusing on making it easy to build intelligent applications that use large-scale machine learning at their core. His previous positions include the Finmeccanica Associate Professor at Carnegie Mellon University and senior researcher at the Intel Research Lab in Berkeley. Carlos is a recipient of a National Science Foundation CAREER Award, an Alfred P. Sloan Fellowship, and the Stanford Centennial Teaching Assistant Award. Carlos was also named one of the 2008 `Brilliant 10' by Popular Science Magazine, received the IJCAI Computers and Thought Award from the top AI conference, and the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Obama.

Read More

icon
More courses by Carlos Guestrin

Free

All courses by Carlos Guestrin

icon
More Artificial Intelligence courses

Free

Free

Free

Free

$79.99

Free

All Artificial Intelligence courses

icon
Reviews

4.8

978 reviews in total

Select a bar to show only those reviews.Select the bar again to show every rating.

Write a review

By Shibhikkiran D on 13-Apr-19

This is course is very informative for a beginner. It helps you to get up and running quick provided you have little basics on Python. You should( sideline on your own interest) also pickup Statistics/Math concepts along each module to make a rewarding experience as you progress through this course.

By Kunal G on 10-Apr-19

it is a very good course if you are a newbie in this area and only know a bit of python, just be careful not to use graphlab, use turicreate instead

By shubham k on 8-Apr-19

this was really learning

By Pritish K on 7-Apr-19

The most useless course on Coursera. I have wasted 3 weeks just trying to install Graphlab and the installation seems infinitely tedious. There is no support from Coursera or University of Michigan to install the software Why do they insist on teaching on a software which have so many known issues and so many students are struggling to install the software. The objective is to learn data analytics and machine learning, not to become a systetm admin and n IT guy.

By Noam K on 2-Apr-19

Nice overview, the case study approach is very useful as well as the actual python notebook assignments.

By Hasan H J on 2-Apr-19

excellent

By Arindam M on 29-Mar-19

It was a really nice course. It will be further helpful if the regression algorithms are discussed. Thank You

By CHIRANJI L C on 28-Mar-19

practical exposure is excellent!

By Arif A on 25-Mar-19

I have a fairly good background in mathematics and have read through major parts of the Deep Learning Textbook by Goodfellow et. al. One year later I wanted to revise ML again. People who are complaining that there is no mathematical or algorithmic rigor in this course need to understand that this is meant to be an introductory course in order to pique interest in the learner and drive him/her to pursue this field further. Heavy focus on math and algorithms straightaway does not work for most people. Hence, I conclude that this is a good intro course which does it's job quite well.

By YASHKUMAR R T on 24-Mar-19

Best course to understand all the fundamentals of machine learning for beginners.

By Sharad J on 23-Mar-19

The ML concept is explained with use cases and demonstrated with python programs which

By Evan S on 11-Mar-19

This course was a great balance between lecture (and lecture quiz) & iPython lecture (and iPython lecture quiz). I like that the answers are multiple choice as opposed to copying and pasting code. That way, any coding errors can be played around with in the notebook first without using up any submission attempts. Emily and Carlos did a great job of keeping the course fun while sticking to the easy-to-understand case-study approach.

Showing 12 of 978 reviews

Machine Learning Foundations: A Case Study Approach
Visit course on Coursera Write a review
  • Video Duration: icon 24h
  • Price: Free
  • Provider: icon Coursera
  • University: icon University of Washington
  • Language: icon English
  • Certificate: icon Yes

Coursera Coupon Price Tracker

Udemy price history

For any issues or feature requests please email CourseDuck's Co-Founder at mkuhlman12@gmail.com